Band selection technique based on binary modified equilibrium optimizer for hyperspectral image classification

The performance of hyperspectral image (HSI) classification can be enhanced by utilizing a metaheuristic technique with high exploration and exploitation capabilities for band selection. We design a framework for the improved classification of HSIs by utilizing a binary modified equilibrium optimizer (BMEO) for band selection. First, the proposed framework preprocesses the HSI data using the Gaussian filter to remove the undesired variations. Band selection is performed on the preprocessed HSI data using a BMEO, which exhibits balanced and high exploration and exploitation capabilities. The proposed V-shape transfer function is used for enhancing the exploration capabilities of the BMEO. An analysis for band selection is performed by comparing the reflectance spectra, correlation of each band with ground truth, and correlation between the bands before and after the band selection. The classification of HSI data by utilizing selected bands was done using the classifier selected by classifier analysis. The proposed framework outperforms 11 state-of-the-art techniques, including filter, wrapper, and deep learningbased techniques. The 95.85%, 98.13%, and 98.31% overall accuracies on the Indian pine, Pavia University, and Salinas datasets, respectively, show the significance of the proposed framework. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)